The justice system has increasingly employed AI techniques to enhance efficiency, yet limitations remain in improving the quality of decision-making, particularly regarding transparency and explainability needed to uphold public trust in legal AI. To address these challenges, we propose a large language model based multi-agent framework named AgentsBench, which aims to simultaneously improve both efficiency and quality in judicial decision-making. Our approach leverages multiple LLM-driven agents that simulate the collaborative deliberation and decision making process of a judicial bench. We conducted experiments on legal judgment prediction task, and the results show that our framework outperforms existing LLM based methods in terms of performance and decision quality. By incorporating these elements, our framework reflects real-world judicial processes more closely, enhancing accuracy, fairness, and society consideration. AgentsBench provides a more nuanced and realistic methods of trustworthy AI decision-making, with strong potential for application across various case types and legal scenarios.

In recent years, the utilization of large language models for natural language dialogue has gained momentum, leading to their widespread adoption across various domains. However, their universal competence in addressing challenges specific to specialized fields such as law remains a subject of scrutiny. The incorporation of legal ethics into the model has been overlooked by researchers. We asserts that rigorous ethic evaluation is essential to ensure the effective integration of large language models in legal domains, emphasizing the need to assess domain-specific proficiency and domain-specific ethic. To address this, we propose a novelty evaluation methodology, utilizing authentic legal cases to evaluate the fundamental language abilities, specialized legal knowledge and legal robustness of large language models (LLMs). The findings from our comprehensive evaluation contribute significantly to the academic discourse surrounding the suitability and performance of large language models in legal domains.

The judiciary has historically been conservative in its use of Artificial Intelligence, but recent advances in machine learning have prompted scholars to reconsider such use in tasks like sentence prediction. This paper investigates by experimentation the potential use of explainable artificial intelligence for predicting imprisonment sentences in assault cases in New Zealand's courts. We propose a proof-of-concept explainable model and verify in practice that it is fit for purpose, with predicted sentences accurate to within one year. We further analyse the model to understand the most influential phrases in sentence length prediction. We conclude the paper with an evaluative discussion of the future benefits and risks of different ways of using such an AI model in New Zealand's courts.

The laws that govern affluent clients and large institutions are numerous, intricate and applied by highly sophisticated practitioners. In this section of society, rules proliferate, lawsuits abound, and the cost of legal services grows much faster than the cost of living. For the bulk of the population, however, the situation is very different. Access to the courts may be open in principle. In practice, however, most people find their legal rights severely compromised by the cost of legal services, the baffling complications of existing rules and procedures, and the long, frustrating delays involved in bringing proceedings to a conclusion . . .

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A New Jersey man who used dating apps to lure and kill three women five years ago was sentenced Wednesday to 160 years in prison after a trial in which it was revealed that friends of one victim did their own detective work on social media to ferret out the suspect. Khalil Wheeler-Weaver, 25, sat motionless as the judge gave the sentence in state court in Newark. The sentencing was preceded by emotional statements by family members of victims Robin West and Sarah Butler.

Not as a defendant -- it would have been a novel case had it been so -- but as an aid to help the magistrate with sentencing. Trends elsewhere suggest something more. But if a machine should one day sit at the bench presiding over a court battle between men and men, then it will be a surrender most ominous. There are at least two reasons why we should not defer to machines. It is true that AI can do many complicated things.

Thanks to today's "Internet of Things" (IoT), there is an "automation" for almost every aspect of our lives. From such mundane if not downright silly things as kitchen faucets that activate on voice command, to the impressive -- massive shipping warehouses run by robotics -- many aspects of life today go beyond that imagined decades ago in science fiction. While we still are waiting for flying cars depicted in the Jetsons television show of the 1960s, or space hotels as portrayed in the sci-fi epic 2001, the array of technologically driven devices available to the average citizen is indeed impressive. Yet, while automation and artificial intelligence simplifies or altogether eliminates many of the activities of day-to-day life, the technology complicates others. For example, how do you program a self-driving car in an emergency situation to choose between the life of a pedestrian or that of its "driver?"

Despite its widespread adoption, Artificial Intelligence still has a long way to go in terms of diversity and inclusion. We try to write a lot about the positive ways in which Artificial Intelligence and other technologies are impacting our world. It's a subject close to our hearts as a company, and quite frankly, something that should be celebrated and shouted about given all the doom and gloom we're so often bombarded with in today's media. From healthcare, and sustainable cities, to climate change and industry, investment in AI is making an impact in many areas. Applications of machine learning and deep learning help shape the trajectories of our daily lives, so much so that we are barely even aware of it.

Sentences handed down by artificial intelligence would be fairer, more efficient, transparent and accurate than those of sitting judges, according to Swinburne researchers. Dean of Swinburne Law School, Professor Dan Hunter, and Swinburne researcher Professor Mirko Bagaric say artificial intelligence (AI) could improve sentencing procedures by removing emotional bias and human error. In a paper for the Criminal Law Journal, Professors Bagaric and Hunter argue that AI sentencing would better identify, sort and calibrate all the variables associated with sentencing, including criminal history, education, drug/alcohol use, emotional motivations and employment. The pair argue that sentencing decisions are often influenced by more than 200 considerations, many of which are variables which have been established prior to court hearings. Professor Bagaric says subconscious bias plays a large part in sentencing in which judges or magistrates hand down harder penalties to offenders of a particular race or background.

Agents found Jorge Rivera in possession of 13 pounds of methamphetamine in Aug. 2017 when he was trying to use a drone to smuggle drugs across the border. A 25-year-old man who was previously arrested after using a drone to smuggle drugs across the U.S.-Mexico border has been sentenced to 12 years in prison, border officials announced. A jury sentenced Jorge Rivera on Wednesday after he was convicted last week of trying to traffic 13 pounds of methamphetamine into the United States during the summer, according to a release from the U.S. Customs and Border Protection. Rivera was detained on the night of Aug. 8, 2017 after a border patrol agent saw a drone flying across the border near the San Ysidro Port of Entry, the original arrest report said. An officer later reportedly located the suspect who was operating the drone and found him in possession of a bag containing "multiple plastic-wrapped packages containing methamphetamine."